In today’s businesses, data should underpin every decision. It can be your most valuable enterprise asset if it is processed, stored, managed, and shared in ways that your employees can find, use, and interpret it. To do this, you need a data integration strategy.

This is part three of an occasional series on data governance (DG) and its enabling technologies. In parts one and two I gave an overview of what data governance is and how it might be undertaken; in this blog, I talk about business glossaries--one of the key DG-enabling technologies--and how to create one.

The ability for cloud data warehouses to provide a single platform that enables organizations to analyze data at any scale is a market changing capability. Sure, much has been made of the ability of applying Artificial Intelligence (AI) to this data to gain insight into new areas, ultimately to increase an organization’s competitiveness. There is however a greater level of impact that this architecture can have that can be independent of AI and ultimately inclusive of AI.

When it comes to automation in the BI industry, I think a lot of vendors are holding back. It’s entirely possible to automate the majority of what data analysts do today, but vendors are reluctant to do this because it would impact data analysts and they’re a big gatekeeper in the buying cycle of analytic software.